Real-time polymerase chain reaction (PCR) has become an important tool in a variety of fields, ranging from medical diagnostics, to forensics and food safety monitoring. The advantages of current PCR technology are rapid detection, qualitative as well as quantitative information and high sensitivity and specificity. These features have made the real-time PCR a technology of choice in fields ranging from pathogen detection to testing for oncogenic mutations in the emerging field of personalized healthcare. See L. Peterson, (2011) Molecular laboratory tests for the diagnosis of respiratory tract infection due to Staphylococcus aureus. Clin. Infect. Dis.; 52 Suppl. 4:S361, S. Anderson, (2011) Laboratory methods for KRAS mutation analysis. Expert Rev. Mol. Diag.; 11:635. Many real-time PCR tests have been validated and harmonized to become standard tools used by hospitals and large-throughput commercial laboratories. However, PCR does have its limitations. With the daily running of PCR assays by a diagnostic laboratory, the problems of false-negative and false-positive results quickly become apparent. See J. Maurer, (2011) Rapid detection and limitations of molecular techniques. Ann. Rev. Food Sci. Technol.; 2:259. Many errors result from poor quality of the collected sample. However, the use of a better mathematical or statistical model during data analysis holds the promise of overcoming at least some of the problems associated with poor input material. See M. Sivaganesan et al., (2010) Improved strategies and optimization of calibration models for real-time PCR absolute quantification. Water Res.; 44:4726.
As an output of a real-time PCR-based diagnostic method, a sample is sometimes classified into one of several categories: mutant and wild-type, infected and not infected, etc. Sensitivity of a method is reduced when samples fall into the “grey area” where the signal appears present but too close to the minimum threshold to be classified into any of the groups. The unclassified pool represents potentially false-negative and false-positive samples for whom the real-time PCR test has failed to deliver an answer. The present invention provides a better statistical tool for minimizing the number of unclassified samples in real-time PCR tests.